CN116154956A - Robustness analysis method for distributed photovoltaic power station system - Google Patents

Robustness analysis method for distributed photovoltaic power station system Download PDF

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CN116154956A
CN116154956A CN202211716923.8A CN202211716923A CN116154956A CN 116154956 A CN116154956 A CN 116154956A CN 202211716923 A CN202211716923 A CN 202211716923A CN 116154956 A CN116154956 A CN 116154956A
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node
network
power
load
nodes
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汪洋
陈彦斌
葛愿
刘翔
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Anhui Polytechnic University
Wuhu Power Supply Co of State Grid Anhui Electric Power Co Ltd
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Anhui Polytechnic University
Wuhu Power Supply Co of State Grid Anhui Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The robustness analysis method of the distributed photovoltaic power station system solves the problem of the robustness analysis method of the photovoltaic power generation system under the condition of high permeability, overcomes the defect that the influence area of the distributed photovoltaic power generation is not fully considered in the robustness analysis of the traditional power system, and facilitates the understanding and analysis of safety personnel.

Description

Robustness analysis method for distributed photovoltaic power station system
Technical Field
The invention belongs to the technical field of photovoltaics, and particularly relates to a robustness analysis method of a distributed photovoltaic power station system.
Background
The rapid development of smart grids brings great convenience to human society, but complex topological structures and interference of external factors also bring more potential safety hazards. A failure of one node in the grid may lead to a cascading failure of another node in the grid, which then propagates throughout the network, which dramatically reduces system robustness until the entire system breaks down. The existing robustness analysis of the power system rarely considers uncertain factors of distributed photovoltaic power generation and impact of short-time access of flexible loads to a power grid. A large amount of flexible loads are connected into a power grid in a short time, partial nodes possibly fail due to overload, and in fact, due to the characteristics of distributed photovoltaic power generation in the cascade failure process, power generation nodes which are not failed exist, the existing method is not fully considered, and the robustness of the evaluation system is affected.
Disclosure of Invention
The invention provides a robustness analysis method of a distributed photovoltaic power station system, and aims to improve the problems. The technical proposal is as follows: a distributed photovoltaic power plant system robustness analysis method, the method comprising the steps of:
step one: acquiring all data of a power grid system containing a distributed photovoltaic power station, wherein the data comprise power plants, substations, loads, routers, switches and the like;
step two: establishing a network model of a distributed photovoltaic power generation system by applying a complex network theory, wherein the network model comprises a power network, a power communication network and an associated network model, and calculating node loads and node capacities in the system;
step three: determining a node with overload load in a power grid, taking the node with overload load as an analysis starting point, firstly removing nodes which are connected with no node in a power grid model and a communication network model, then removing non-power generation nodes which are connected with power generation nodes in the power grid model, secondly removing nodes which are not connected with any node in the power grid model, judging whether the rest of communication components except the maximum communication component contain photovoltaic power generation nodes after removing the nodes, and if so, not taking the rest of communication components as failure nodes; finally, load redistribution is carried out, and a system robustness index after load redistribution is calculated;
step four: and (3) detecting overload for the second time, if the load recalculated by some nodes exceeds the capacity of the nodes in the rest network, removing the nodes, returning to the third step, iterating the whole process until no node in the system can be removed, and calculating the system robustness index.
The further improvement is that: the second step further comprises the following steps: abstracting a power plant, a transformer substation and a load in a power grid into simple power nodes, and abstracting a power transmission line into edges between the nodes in the power grid; in the power communication network, devices such as routers and switches are abstracted into simple communication nodes, communication lines such as cables and optical fibers are abstracted into edges between nodes in the communication network, and in the association part of the two, association relations between the power network and the nodes of the communication network are abstracted into edges in the network.
The further improvement is that: the third step further comprises the following steps: assuming that the load failure ratio of the power network nodes is 1-p, and then the internal nodes of the power network are in failure cascade connection according to the failure condition of a single network node, wherein the failure cascade connection comprises node failure, edge failure and load overload failure; after stabilization, the power network and communication network structure is updated, which is process 1, state 1.
The further improvement is that: the load redistribution method in the third step comprises the following steps: the cascade failure process of the associated network is represented by n, and the total load in unit time is as follows:
Figure BDA0004024042230000031
wherein phi is the set of operational nodes in the power network, l ni The degree of the power network node i in the network cascade failure process n is the degree, and alpha and t are parameters for guaranteeing the normal operation of the associated network.
The further improvement is that: the load of the process n communication node i
Figure BDA0004024042230000032
Expressed as: />
Figure BDA0004024042230000033
Wherein Z represents a node set operable in the power communication network, d ni Representing the degree of node i in the power communication network during the cascading failure process n, τ represents a parameter that controls the load that the power communication node can accommodate, a larger value represents a greater degree of node,the more load is borne; capacity ∈of communication node i>
Figure BDA0004024042230000034
Is +.>
Figure BDA0004024042230000035
Proportional to->
Figure BDA0004024042230000036
Wherein delta c Is a tolerance parameter of the communication node.
The further improvement is that: load of initial node i of power network
Figure BDA0004024042230000037
Is->
Figure BDA0004024042230000038
Wherein beta and->
Figure BDA0004024042230000039
Representing parameters controlling the load, l 0i Is the degree of power network node i before cascade failure, when beta and + ->
Figure BDA00040240422300000310
When the degree of the node in the power network is larger than 0, the load of the node is larger, i>
Figure BDA00040240422300000311
Capacity and->
Figure BDA00040240422300000312
Is in direct proportion to: />
Figure BDA00040240422300000313
Wherein delta p Is a tolerance parameter for the node.
The further improvement is that: the probability that the load of the failed node i is distributed to the neighbor node j is expressed as follows:
Figure BDA00040240422300000314
where f (i) is the set of neighbor nodes of node i, L j The initial load of the neighbor node j is gamma, which is an allocation parameter used for controlling the load allocation probability, and the load increment of the node j is expressed as follows: ΔL j ＝P ij C i Wherein C i Is the failure load of node i, when node i fails, the load of node j is transferred to the adjacent node j, and the load increment of node j is delta L j . The load of node j at this time is: l'. j ＝ΔL j +L j 。
The further improvement is that: the fourth step specifically comprises the following steps:
step 4.1: because the process 1 causes the inter-network connection coupling model to change, some communication network nodes lose power support, so that failure cascade occurs in the communication network, and the communication network and the coupling network structure are updated after stabilization, and the process is a process 2 and a state 2;
step 4.2: because the process 2 causes the inter-network connection coupling model to change, some power network nodes lose control of communication nodes, so that failure cascade connection also occurs in the power network, the power network and the coupling network structure are updated after stabilization, and the process is changed into a process 3 and a state 3;
step 4.3: and repeating the process 2 and the process 3 until the network is stable or completely crashed, stopping the cascade failure of the coupling network, and calculating the robustness index of the system.
The invention has the beneficial effects that: the robustness analysis method of the photovoltaic power generation system under the condition of high permeability is solved, the defect that the influence band of distributed photovoltaic power generation is not fully considered in the robustness analysis of the traditional power system is overcome, and the understanding and analysis of safety personnel are facilitated.
Drawings
Fig. 1 is a flowchart of a robustness analysis method of a photovoltaic power station system provided by the embodiment of the invention.
Detailed Description
The present invention will be further described in detail with reference to examples, which are provided for the purpose of illustration only and are not intended to limit the scope of the present invention.
As shown in fig. 1, the present embodiment provides a robustness analysis method of a distributed photovoltaic power station system, where the method specifically includes the following steps:
s1, acquiring all data of a power grid system comprising a distributed photovoltaic power station, wherein the data comprise power plants, substations, loads, routers, switches and the like.
S2, a network model of the distributed photovoltaic power generation system is established by applying a complex network theory, the network model comprises a power network, a power communication network and an associated network model, and node loads and node capacities in the system are calculated.
S3, determining a node with overload load in the power network, taking the node with overload load as an analysis starting point, firstly removing nodes which are not connected with any node in the power network model and the communication network model, then removing non-power generation nodes which are not connected with power generation nodes in the power network model, secondly removing nodes which are not connected with any node in the power network model in the communication network model, judging whether the communication components except the maximum communication component after removing the nodes contain photovoltaic power generation nodes, and if so, not taking the communication components as failure nodes. And finally, carrying out load redistribution, and calculating a system robustness index after load redistribution.
S4, the second overload detection is carried out, if the load recalculated by some nodes exceeds the capacity of the nodes in the rest network, the nodes are removed, and the third step is carried out, the whole process is iterated until no node in the system can be removed, and the system robustness index is calculated. Further, the step S2 specifically includes the following steps:
the power plants, substations and loads in the power grid are abstracted to be simple power nodes, and the transmission lines are abstracted to be edges between the nodes in the power grid. In the power communication network, devices such as routers and switches are abstracted into simple communication nodes, communication lines such as cables and optical fibers are abstracted into edges between nodes in the communication network, and in the association part of the two, association relations between the power network and the nodes of the communication network are abstracted into edges in the network.
Further, the step S3 specifically includes the following steps:
assuming that the load failure ratio of the power network nodes is 1-p, then the internal nodes of the power network are in failure cascade connection according to the failure condition of the single network node, wherein the failure cascade connection comprises node failure, edge failure and load overload failure. After stabilization, the power network and communication network structure is updated, which is process 1, state 1. Further, the load redistribution method includes:
the cascade failure process of the associated network is represented by n, and the total load in unit time is as follows:
Figure BDA0004024042230000061
wherein phi is the set of operational nodes in the power network, l ni The degree of the power network node i in the network cascade failure process n is the degree, and alpha and t are parameters for guaranteeing the normal operation of the associated network.
Further, the load of the process n communication node i
Figure BDA0004024042230000062
Expressed as:
Figure BDA0004024042230000063
wherein Z represents a node set operable in the power communication network, d ni The degree of a node i in the power communication network in the cascade failure process n is represented, τ represents a parameter for controlling the load which can be accommodated by the power communication node, and the larger the degree of the node is, the more load is born. Capacity of communication node i
Figure BDA0004024042230000064
Is +.>
Figure BDA0004024042230000065
In direct proportion to each other,
Figure BDA0004024042230000066
wherein delta c Is a tolerance parameter of the communication node.
Further, the load of the initial node i of the power network
Figure BDA0004024042230000067
Is that
Figure BDA0004024042230000068
Wherein beta and
Figure BDA0004024042230000069
representing parameters controlling the load, l 0i Is the degree of power network node i before cascade failure, when beta and + ->
Figure BDA00040240422300000610
When the degree of the node in the power network is larger than 0, the load of the node is larger, i>
Figure BDA00040240422300000611
Capacity and->
Figure BDA00040240422300000612
Is in direct proportion to:
Figure BDA00040240422300000613
wherein delta p Is a tolerance parameter for the node.
Further, the probability that the load of the failed node i is allocated to the neighbor node j is expressed as:
Figure BDA00040240422300000614
where f (i) is the set of neighbor nodes of node i, L j Is the initial load of the neighbor node j, and gamma is the scoreAnd a parameter for controlling the load distribution probability. The node j load delta is expressed as:
ΔL j ＝P ij C i
wherein C is i Is the failure load of node i, when node i fails, the load of node j is transferred to the adjacent node j, and the load increment of node j is delta L j . The load of node j at this time is:
L' j ＝ΔL j +L j 。
further, the calculation formula of the robustness index of the system is as follows:
1) Load loss ratio:
Figure BDA0004024042230000071
wherein L is max Representing the total load demand of the whole power grid, wherein the load loss of the power grid is as follows:
Figure BDA0004024042230000072
where L is the total load of the grid and G is the total power generation of the grid.
2) Index of damage degree:
Figure BDA0004024042230000073
wherein, the damage degree index of the information network structure:
Figure BDA0004024042230000074
wherein c i As a communication node damage factor, the communication node i is disconnected from the dispatching center node, and c is recorded i =1, an isolated communication node, whereas c i ＝0；
The damage degree index of the power grid structure:
Figure BDA0004024042230000081
wherein e i As a power network node damage factor, if the load node and the transmission node are disconnected from the power generation node, e i =1, otherwise, e i ＝0。
Delta represents the weight parameter of the different networks of the two-layer network:
Figure BDA0004024042230000082
/>
3) Minimum removable power generation node number MRGNN
The maximum number of connected nodes is a good performance index in the robustness analysis of the traditional system, but when the system contains a large number of distributed photovoltaics, because of the characteristics of the large number of the distributed photovoltaics and wide distribution, a plurality of power generation nodes exist in a power grid, and at the moment, as long as the connected components exist in the network, the nodes in the connected components can normally operate and cannot be regarded as faults. Therefore, the traditional robustness index has limitation, and the minimum removed power generation node number which leads to the whole system breakdown is proposed to characterize the system performance.
Further, the step S4 specifically includes the following steps:
s41, because the inter-network connection coupling model changes in the process 1, some communication network nodes lose power support, so that failure cascade connection can also occur in the communication network, and the communication network and the coupling network structure are updated after stabilization, and the process is the process 2 and the state is the state 2.
S42, because the inter-network connection coupling model is changed in the process 2, some power network nodes may lose control of the communication nodes, so that failure cascading can also occur in the power network, and the power network and the coupling network structure are updated after the power network and the coupling network structure are stabilized, and the process is changed into the process 3 and the state 3. S43, repeating the process 2 and the process 3 until the network is stable or completely crashed, stopping the cascade failure of the coupling network, and calculating the robustness index of the system.

Claims (8)

1. A robustness analysis method of a distributed photovoltaic power station system is characterized by comprising the following steps of: the method comprises the following steps:
step one: acquiring all data of a power grid system containing a distributed photovoltaic power station, wherein the data comprise power plants, substations, loads, routers, switches and the like;
step two: establishing a network model of a distributed photovoltaic power generation system by applying a complex network theory, wherein the network model comprises a power network, a power communication network and an associated network model, and calculating node loads and node capacities in the system;
step three: determining a node with overload load in a power grid, taking the node with overload load as an analysis starting point, firstly removing nodes which are connected with no node in a power grid model and a communication network model, then removing non-power generation nodes which are connected with power generation nodes in the power grid model, secondly removing nodes which are not connected with any node in the power grid model, judging whether the rest of communication components except the maximum communication component contain photovoltaic power generation nodes after removing the nodes, and if so, not taking the rest of communication components as failure nodes; finally, load redistribution is carried out, and a system robustness index after load redistribution is calculated;
step four: and (3) detecting overload for the second time, if the load recalculated by some nodes exceeds the capacity of the nodes in the rest network, removing the nodes, returning to the third step, iterating the whole process until no node in the system can be removed, and calculating the system robustness index.
2. A distributed photovoltaic power plant system robustness analysis method as claimed in claim 1, characterized in that: the second step further comprises the following steps: abstracting a power plant, a transformer substation and a load in a power grid into simple power nodes, and abstracting a power transmission line into edges between the nodes in the power grid; in the power communication network, devices such as routers and switches are abstracted into simple communication nodes, communication lines such as cables and optical fibers are abstracted into edges between nodes in the communication network, and in the association part of the two, association relations between the power network and the nodes of the communication network are abstracted into edges in the network.
3. A distributed photovoltaic power plant system robustness analysis method as claimed in claim 1, characterized in that: the third step further comprises the following steps: assuming that the load failure ratio of the power network nodes is 1-p, and then the internal nodes of the power network are in failure cascade connection according to the failure condition of a single network node, wherein the failure cascade connection comprises node failure, edge failure and load overload failure; after stabilization, the power network and communication network structure is updated, which is process 1, state 1.
4. A distributed photovoltaic power plant system robustness analysis method as claimed in claim 1, characterized in that: the load redistribution method in the third step comprises the following steps: the cascade failure process of the associated network is represented by n, and the total load in unit time is as follows:
Figure FDA0004024042220000021
wherein phi is the set of operational nodes in the power network, l ni The degree of the power network node i in the network cascade failure process n is the degree, and alpha and t are parameters for guaranteeing the normal operation of the associated network.
5. The method for analyzing robustness of the distributed photovoltaic power station system according to claim 4, wherein: the load of the process n communication node i
Figure FDA0004024042220000022
Expressed as: />
Figure FDA0004024042220000023
Wherein Z represents a node set operable in the power communication network, d ni The degree of a node i in the power communication network in the cascade failure process n is represented, τ represents a parameter for controlling the load which can be accommodated by the power communication node, and the larger the degree of the node is, the more load is born; capacity of communication node i
Figure FDA0004024042220000024
Is +.>
Figure FDA0004024042220000025
Proportional to->
Figure FDA0004024042220000026
Wherein delta c Is a tolerance parameter of the communication node.
6. The method for analyzing robustness of the distributed photovoltaic power station system according to claim 5, wherein: load of initial node i of power network
Figure FDA0004024042220000031
Is->
Figure FDA0004024042220000032
Wherein beta and->
Figure FDA0004024042220000033
Representing parameters controlling the load, l 0i Is the degree of power network node i before cascade failure, when beta and + ->
Figure FDA0004024042220000034
When the degree of the node in the power network is larger than 0, the load of the node is larger, i>
Figure FDA0004024042220000035
Capacity and->
Figure FDA0004024042220000036
Is in direct proportion to: />
Figure FDA0004024042220000037
Wherein delta p Is a tolerance parameter for the node.
7. The method for analyzing robustness of the distributed photovoltaic power station system according to claim 6, wherein: the probability that the load of the failed node i is distributed to the neighbor node j is expressed as follows:
Figure FDA0004024042220000038
where f (i) is the set of neighbor nodes of node i, L j The initial load of the neighbor node j is gamma, which is an allocation parameter used for controlling the load allocation probability, and the load increment of the node j is expressed as follows: ΔL j ＝P ij C i Wherein C i Is the failure load of node i, when node i fails, the load of node j is transferred to the adjacent node j, and the load increment of node j is delta L j The load of node j at this time is: l'. j ＝ΔL j +L j 。
8. A distributed photovoltaic power plant system robustness analysis method as claimed in claim 1, characterized in that: the fourth step specifically comprises the following steps:
step 4.1: because the process 1 causes the inter-network connection coupling model to change, some communication network nodes lose power support, so that failure cascade occurs in the communication network, and the communication network and the coupling network structure are updated after stabilization, and the process is a process 2 and a state 2;
step 4.2: because the process 2 causes the inter-network connection coupling model to change, some power network nodes lose control of communication nodes, so that failure cascade connection also occurs in the power network, the power network and the coupling network structure are updated after stabilization, and the process is changed into a process 3 and a state 3;
step 4.3: and repeating the process 2 and the process 3 until the network is stable or completely crashed, stopping the cascade failure of the coupling network, and calculating the robustness index of the system.
CN202211716923.8A 2022-12-28 2022-12-28 Robustness analysis method for distributed photovoltaic power station system Pending CN116154956A (en)

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CN113282881A (en) * 2021-03-09 2021-08-20 安徽工程大学 Electric power information physical system robustness analysis method based on reachable matrix

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Publication number Priority date Publication date Assignee Title
CN108923469A (en) * 2018-05-29 2018-11-30 中国科学院电工研究所 A kind of New-energy power system cascading failure analysis method
CN110768260A (en) * 2019-09-12 2020-02-07 南京邮电大学 Power grid cascading failure model building method based on electrical betweenness
CN113282881A (en) * 2021-03-09 2021-08-20 安徽工程大学 Electric power information physical system robustness analysis method based on reachable matrix

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